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开发和验证一种半自动和无监督的方法,用于从CT对大腿骨进行细分
Alessandra Aldieri1,2, Riccardo Biondi3,4, Antonino A La Mattina5,6
1PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy. alessandra.aldieri@polito.it.
Scientific reports
|March 29, 2024
概括
一种新的半自动化方法从CT图像中对大腿骨进行细分,与手动方法相比,显著提高了准确性和速度. 这一进步通过提供更可靠的骨质几何数据来增强in silico骨折预测模型.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 计算解剖学的计算解剖学
背景情况:
- 基于量化计算机断层扫描 (QCT) 的in silico模型比传统方法提供了更好的部骨折预测准确度.
- 在CT图像中精确的股骨细分是一个关键的,但具有挑战性的,耗时的,和操作员依赖的手动过程.
研究的目的:
- 开发和评估从CT图像中对大腿骨进行细分的半自动化程序.
- 将半自动化方法的性能与使用几何和in silico骨折风险指标的手动细分进行比较.
主要方法:
- 开发了一种使用骨和关节增强过器和图形切割算法的半自动细分程序.
- 该方法对10名受试者进行了评估,将其输出与标准手动细分进行了比较.
- 用豪斯多夫距离,差异结合比率,对CT扫描的定性忠实度和骨骨折风险评估来评估性能.
主要成果:
- 半自动化方法与手工内部细分变化相比,显示出显著更高的豪斯多夫距离和差异结合比率,表明了明显和可重复的细分.
- 定性评价显示,半自动化程序在对原始CT数据的忠实性方面明显优越 (p < 0.001).
- 在骨骨折风险评估中,手动和半自动方法的细分之间没有显著差异 (R2 = 0.99).
结论:
- 拟议的半自动化股骨细分程序更有效,并提供比手动方法更好的细分质量.
- 这种技术可以增强基于CT的模型,用于部骨折预测.
- 该方法适用于对大型图像数据集进行细分,以训练和验证自动细分算法.
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